Artist Style Transfer Via Quadratic Potential
arXiv:1902.11108
Abstract
In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph. We train our neural networks in adversarial setting via recently introduced quadratic potential divergence for stable learning process. To further improve the quality of generated artist stylized images we also integrate some of the recently introduced deep learning techniques in our method. To our best knowledge this is the first attempt towards artist style transfer via quadratic potential divergence. We provide some stylized image samples in the supplementary material. The source code for experimentation was written in PyTorch and is available online in my GitHub repository.
arXiv comment8 pages, 3 figures, uses nips_2018.sty, renamed the network to CycleGAN-QP for maintaining consistency with work
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